A Deep learning based Safe Travel Advisory Tool for Covid-19

Looking at the massive spread of SARS CoV2(COVID-19), it not only requires medical solutions at this point but different alternatives must also be examined to prevent its contagious nature getting its hands on a large number of individuals. Getting some prior information before its actual cause can help us to prepare ourselves to fight this pandemic better. It can assist authorities and administration to make better decisions in relatively less time to figure out the most suitable solutions. Since it is difficult to devise a permanent solution to this kind of pandemic, such data analysis can be used to strategize ourselves to cope with it. This study focuses on the forecasting of the number of active cases using deep neural networks. The models used in this approach are Multilayer Perceptron(MLP), Convolution Neural Networks(CNN) and Long Short Term Memory(LSTM). The performance of all three models is analyzed and although all of them are reasonably well, the MLP model outperforms the other two. These models can be used to predict the number of cases on a given day and a potential future outbreak.